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Seasonal Influence of Indonesian Throughflow in the Southwestern Indian Ocean

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Seasonal Influence of Indonesian Throughflow in the Southwestern Indian Ocean Seasonal influence of Indonesian Throughflow in the southwestern Indian Ocean Lei Zhou and Raghu Murtugudde Earth System Science Interdisciplinary Center, College Park, Maryland Markus Jochum National Center for Atmospheric Research, Boulder, Colorado Lei Zhou Address: Computer & Space Sciences Bldg. 2330, University of Maryland, College Park, MD 20742 E-mail: [email protected] Phone: 301-405-7093 1 Abstract The influence of the Indonesian Throughflow (ITF) on the dynamics and the thermodynamics in the southwestern Indian Ocean (SWIO) is studied by analyzing a forced ocean model simulation for the Indo-Pacific region. The warm ITF waters reach the subsurface SWIO from August to early December, with a detectable influence on weakening the vertical stratification and reducing the stability of the water column. As a dynamical consequence, baroclinic instabilities and oceanic intraseasonal variabilities (OISVs) are enhanced. The temporal and spatial scales of the OISVs are determined by the ITF-modified stratification. Thermodynamically, the ITF waters influence the subtle balance between the stratification and mixing in the SWIO. As a result, from October to early December, an unusual warm entrainment occurs and the SSTs warm faster than just net surface heat flux driven warming. In late December and January, signature of the ITF is seen as a relatively slower warming of SSTs. A conceptual model for the processes by which the ITF impacts the SWIO is proposed. 2 1. Introduction Sea surface temperature (SST) variations in the southern Indian Ocean are generally modest. But they are significantly larger in the southwestern Indian Ocean (SWIO, Annamalai et al. 2003). In an analysis of observational data, Klein et al. (1999) reported that surface heat flux anomalies explain the basin-wide warming over most of the tropical Indian Ocean, the only exception being the SWIO. Masumoto and Meyers (1998) argued that the large SST variations in the SWIO are mainly attributable to the forced Rossby waves propagating in from the southeastern Indian Ocean (SEIO). Moreover, due to Ekman pumping, the SWIO is an upwelling region, where the subsurface thermocline variability has a significant influence on SSTs (Murtugudde and Busalacchi 1999, Schott et al. 2002). Xie et al. (2002) further concluded that much of the SST variability in the SWIO is not due to local winds or surface heat fluxes, but is instead due to oceanic Rossby waves that arrive from the east modifying the temperature of upwelled waters. The oceanic intraseasonal variabilities (OISVs) have been shown by several recent studies to be important for the heat budget in the oceanic mixed layer, via their impact on horizontal heat transport and related non-linear advective effects (Waliser et al. 2003, 2004; Jochum and Murtugudde 2005). In the southern Indian Ocean, Du et al. (2005) studied the seasonal mixed layer heat budget by analyzing a high-resolution OGCM to argue that maximum upwelling occurred when the Indonesian Throughflow (ITF) reaches its annual maximum. Moreover, the warm advection associated with the ITF in their model neutralized the cold upwelling, leading to a slight damping of SST variability off Java and Sumatra (also see Murtugudde et al. 1998). They also found that the residual heat flux in their OGCM was not negligible during boreal winter, which might be 3 attributable to the local intraseasonal variations. By comparing the model runs with open and closed ITF, Hirst and Godfrey (1993) noted that maximum temperature and salinity perturbations are along the thermocline in the open ITF run in the region between 28°S and 8°S, in addition to a strong vertical velocity shear. To the best of our knowledge, there have been very few observational and modeling studies of OISVs thus far in the SWIO (e.g., Murtugudde and Busalacchi 1999). We showed in Zhou et al. (2007) that the OISVs are strong from October to January, when they are mainly strengthened by the baroclinic instabilities. Some interesting questions remain unexplored. Since the OISVs in the SWIO are hypothesized to originate in the east (Xie et al. 2002; Zhou et al. 2007), what is the relation between the ITF and the OISVs in SWIO? Do the OISVs in SWIO have a significant impact on local SST variability? In this study, we attempt to address these questions by analyzing an OGCM output along with the World Ocean Atlas data (WOA, Conkright et al. 2002). In Section 2, the model is described and compared with satellite SST product (comparisons to altimeter data were presented in Zhou et al. 2007). In Section 3, the westward propagation of ITF waters and its primary influence in the SWIO is examined. In Section 4, the dynamical influence of the ITF in the SWIO is discussed. Section 5 discusses the thermodynamic influence of the ITF, focusing on SST variability and the unusual warm entrainment. A conceptual model for the influence of ITF in SWIO, along with Discussion and Conclusion, is presented in Section 6. 2. Model description and comparison 4 The SWIO has noticeably large OISVs (Waliser et al. 2003; Jochum and Murtugudde 2005), which are mainly attributable to the oceanic internal baroclinic instabilities (Zhou et al. 2007). The wind-forcing used here are weekly mean climatologies which retain very little energy in the intraseasonal band, consistent with the hypothesis that the OISVs are generated internally by the ocean. Interannual variability in the atmospheric intraseasonal variabilities (AISVs) may in fact modulate these OISVs especially since the oceanic background state itself varies on interannual time-scales. To simplify the analyses, it is assumed that the interannual variability in the AISV forcing will not significantly affect the basic processes of oceanic internal instabilities and the generation of OISVs. SST anomalies associated with the OISVs are a significant fraction of the total SST variability in the SWIO which supports our assumption (Jochum and Murtugudde 2005). Therefore, climatological model outputs are analyzed in this paper, from which the responses to the intraseasonal atmospheric forcing are largely removed. The model used is a reduced gravity, sigma-coordinate, primitive equation OGCM, with a horizontal resolution of 1/3º in latitude and 1/2º in longitude over the Indo-Pacific domain covering 32°E-76°W, 30°S-30°N (Murtugudde et al. 1996, 1998). There are 15 sigma layers in the vertical below the variable depth mixed layer with a resolution of ~15 m in the thermocline in the SWIO, so that vertical oscillations in the interior ocean can be adequately resolved. Surface mixed layer is determined by the hybrid mixing scheme of Chen et al. (1994) which explicitly accounts for the entrainment induced by the surface turbulent kinetic energy, shear-driven dynamic instability mixing, and convective mixing to remove static instabilities. The last sigma-layer thickness is a prognostic variable whereas the other sigma layers are specified constant fractions of the total depth below 5 the mixed layer to the motionless abyssal layer. The temperature and salinity from the climatological WOA (Conkright et al. 2002) are interpolated onto the sigma layers and the model temperature and salinity are relaxed to the interpolated WOA in the sponge layer (south of 25°S) with a time-scale of 5 days. The model is driven by the climatological weekly NCEP Reanalysis winds as mentioned above (see Murtugudde et al. 2000 for details) with the surface heat fluxes computed by an advective atmospheric mixed layer model which allows SSTs to be directly determined by the variables internally calculated in the model, such as the air temperature and humidity (Seager et al. 1995; Murtugudde et al. 1996). The model outputs for all the analyses presented here are weekly mean fields from the last 20 years of a 270-year simulation. The OGCM has been reported in many previous applications, demonstrating its ability to simulate the ocean dynamics and thermodynamics reasonably well in the tropical oceans. Mean SSTs (temperatures of the surface mixed layer in the model) and standard deviations (STDs) of the model outputs and the gridded AVHRR data (McClain et al. 1985) are shown in Fig. 1. Model simulations match satellite observations very well in the tropics; e.g. the 28°C isotherm in the model, which is the critical threshold for the occurrence of deep convection in the atmosphere, is almost identical to observations. However, there are obvious cold biases, as the Bay of Bengal, where simulated SSTs are generally cooler than the observations by ~0.5°C. Maximum cold bias (~1°C) occurs between 10°S and 20°S centered at 15°S, 70°E. STDs of the SSTs are generally small (< 1°C) in the tropical Indian Ocean, where the SSTs are higher than 28°C. In the SWIO, STDs are generally larger than 1.5°C. Moreover, large STDs to the northwest of Australia and around the northern tip of Madagascar are also well resolved. These simulated 6 features are consistent with the observations. However, SST variances are smaller than observations in the western Arabian Sea and the northern Bay of Bengal by ~0.5°C and around 20°S, 80°E by ~1°C. Small SST variances are mainly attributable to the removal of AISVs from the external wind forcing. Local maximum in SST variance centered around 12°S, which is attributable to the internal variability of the Indian Ocean, has been discussed in Jochum and Murtugudde (2005). In this paper, we focus on the variabilities in the SWIO, which is identified by a rectangle in Fig. 1(a). The cold bias and small SST variance to the south of 10°S do not have significant impact on the following discussions. We conclude that the model performance is acceptable for this study in the low latitudes in the Indian Ocean. 3.
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